TY - GEN
T1 - Electroencephalographic spectral analysis to help detect depressive disorder
AU - Apsari, Ratna A.
AU - Wijaya, Sastra K.
N1 - Funding Information:
This research is supported by the grant of PUTI (Publikasi Terindeks Internasional) from Universitas Indonesia, 2020, under the contract number NKB-617/UN2.RST/HKP.05.00/2020.
Publisher Copyright:
© 2020 IEEE.
PY - 2020/10/6
Y1 - 2020/10/6
N2 - The increasing prevalence of depressive disorder (also known as major depressive disorder or MDD), especially in the younger generations, has brought urgency upon the importance of good mental health. Moreover, depression has proven to increase the risk of cardiovascular diseases, along with the severity of those diseases. Depressive disorders are oftentimes not diagnosed or misdiagnosed, because some of the symptoms are similar to those of other illnesses. Therefore, an electroencephalography-based system that could help diagnose this illness using a more quantitative approach is necessary to be developed. The goal of this study is to make a machine learning-based classification program using EEG signals to aid for the diagnostics of depression. EEG data of 19 channels were obtained from two data sources, Hospital Universiti Sains Malaysia and Leipzig Study of Mind, Body, and Emotion. The EEG data consisted of 31 depressed subjects and 30 healthy controls during resting conditions. These signals were processed using two different methods, which were wavelet transformation and Power Spectral Density (PSD). Relative power ratio and average alpha asymmetry were calculated for feature extraction. The classifier used was a feedforward neural network with cross validation. The highest achieved results were 83,6% accuracy using the wavelet method and 77,5% accuracy using the PSD method.
AB - The increasing prevalence of depressive disorder (also known as major depressive disorder or MDD), especially in the younger generations, has brought urgency upon the importance of good mental health. Moreover, depression has proven to increase the risk of cardiovascular diseases, along with the severity of those diseases. Depressive disorders are oftentimes not diagnosed or misdiagnosed, because some of the symptoms are similar to those of other illnesses. Therefore, an electroencephalography-based system that could help diagnose this illness using a more quantitative approach is necessary to be developed. The goal of this study is to make a machine learning-based classification program using EEG signals to aid for the diagnostics of depression. EEG data of 19 channels were obtained from two data sources, Hospital Universiti Sains Malaysia and Leipzig Study of Mind, Body, and Emotion. The EEG data consisted of 31 depressed subjects and 30 healthy controls during resting conditions. These signals were processed using two different methods, which were wavelet transformation and Power Spectral Density (PSD). Relative power ratio and average alpha asymmetry were calculated for feature extraction. The classifier used was a feedforward neural network with cross validation. The highest achieved results were 83,6% accuracy using the wavelet method and 77,5% accuracy using the PSD method.
KW - Average alpha asymmetry
KW - Depressive disorder
KW - EEG
KW - Feedforward neural network
KW - Relative power ratio
UR - http://www.scopus.com/inward/record.url?scp=85112622054&partnerID=8YFLogxK
U2 - 10.1109/IBIOMED50285.2020.9487614
DO - 10.1109/IBIOMED50285.2020.9487614
M3 - Conference contribution
AN - SCOPUS:85112622054
T3 - IBIOMED 2020 - Proceedings of the 37th International Conference on Biomedical Engineering
SP - 13
EP - 18
BT - IBIOMED 2020 - Proceedings of the 37th International Conference on Biomedical Engineering
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 37th International Conference on Biomedical Engineering, IBIOMED 2020
Y2 - 6 October 2020 through 8 October 2020
ER -
